Data management is the process of collecting, organizing, protecting, and storing data for decision-making purposes. In the modern digital economy, companies have access to more data than ever before. This data is the foundation of business intelligence (BI).
Data Management Procedures and Tasks
Data management involves the following:
- Collecting, processing, validating, and storing data
- Integrating data from different sources – structured and unstructured data
- Ensuring high data availability and disaster recovery
- Securing or protecting data from loss, theft, privacy violation, and breaches
Types of Data
Data classification is vast, but this article will discuss the fundamental data types that are:
a) Structured Data
Structured data has a well-defined structure that is easy to enter, update, store, query, or analyze. That standard enables machines to read it without hurdles. Moreover, you will find structured data in a tabular format, such as the data stored in a database.
The examples of structured data include:
- Names
- Dates
- Email addresses
- Telephone numbers
- Currency
- Prices
- Weights
- Ages
- Heights
b) Unstructured Data
Unlike structured data, unstructured data does not follow conventional data models, making it difficult to store and manage. It often demands human intervention. Although that is the case, data scientists are working on machine learning and artificial intelligence programs to manage this challenge.
Unstructured data exist as datasets (large collections of files). What’s more, both humans and machines can generate this type of data in a textual or non-textual format. Unstructured data includes:
- Video files
- Images
- Audio files
- Survey
- Social media posts
- SMS messages
c) Big Data
It refers to large or complex data sets that traditional data processing apps cannot handle. Above all, big data is a combination of structured, semi-structured, and unstructured data.
How do organizations collect big data?
They use machine learning projects, predictive modeling, and advanced analytics applications. Equally important, IoT is a new source of big data. This data originates from a network of connected devices.
Did you know that emails are semi-structured?
An email header contains structured elements such as the date, language, and recipient’s email address. On the other hand, the body will have a message, which is unstructured.
More Types of Data
Qualitative data or quantitative data are the other common types of data.
i) Qualitative Data
This type of data does not exist in a countable format. The two subcategories of qualitative data are:
- Nominal Data – A set of values without a natural ordering e.g. gender (women, men), color (blue, red, yellow), and marital status (married, divorced, widowed). You cannot compare marital status. For instance, married is greater than divorced.
- Ordinal Data – They have a natural ordering, and we can sort them according to their name tag. For example, small < medium < large
Other instances of ordinal data are:
- Grades (A, B, or C)
- The first, second, and third person in a competition
ii) Quantitative Data
Quantitative data presents things using numbers. Therefore, this data can be counted or measured in numerical values. The two types of quantitative data are:
- Discrete Data – Numerical values that exist as integers or whole numbers, and they can appear on charts (bar charts, pie charts, and tally charts)
- Continuous Data – Refers to fractional numbers (decimal values) that can be represented using graphs. Continuous data represents precise measurements of nearly any numeric value. Examples are the temperature and weight values.
Why is Data Management Important?
Data management practices allow people to search and find trusted data for their queries. Furthermore, these approaches enhance the visibility, reliability, security, and scalability of data.
In other words, effective data management has the following benefits:
- Simplify the process of searching for the right data for analysis.
- Helps to minimize potential errors related to data.
- It protects data against loss, theft, and breaches.
- Data management enables scaling and usage of data without repeatable processes of keeping it up-to-date.
Data Management Systems
The following are the most common data management systems and components:
- Database management systems (DBMS)
- Data warehouses and data lakes
- Master Data Management (MDM)
Conclusion
Importantly, reliable data management techniques consolidate your organization. It simplifies the coordination of activities and decision-making processes. Ultimately, that leads an enterprise toward achieving its goals and objectives.
Today’s world requires you to have a good range of data science skills. It is also imperative to understand various data types and know when to apply them. This topic is too vast.
However, have you ever come across the following data types – integers, floating points, longs, shorts, strings, characters, or Booleans?
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Cheers!